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• W2973048552 abstract "Abstract A large number of operational and geometrical parameters are involved in the optimization of counterflow vortex tubes. The number of parameters is so important that most researchers relied on a one factor at a time approach which cannot capture the possible interactions between the relevant parameters. Additionally, literature contains very few information on how to design properly a vortex tube for given inlet conditions. To identify the most important features, two approaches are used: artificial neural networks (ANNs) and thermodynamic modeling. Both methods could reproduce accurately the cold outlet temperature of vortex tubes from the literature. To achieve this, the ANN uses only the inlet pressure, the cold mass fraction, the length to the diameter ratio and the cold outlet diameter to the vortex diameter ratio. The output from another ANN is the inlet mass flow rate. This second ANN uses only the inlet pressure, the length to diameter ratio, the total inlet nozzles’ area and the vortex tube diameter as inputs to do the calculation. The most important parameter in the thermodynamic model is the inlet pressure to cold outlet pressure ratio. It is shown that the variations of this ratio by friction in the inlet nozzles are not negligible and should be considered in future vortex tubes’ design. The Bodewadt boundary layer flow between the inlet and the cold outlet affects also significantly the vortex tube performance." @default.
• W2973048552 created "2019-09-19" @default.
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• W2973048552 date "2019-12-01" @default.
• W2973048552 modified "2023-09-24" @default.
• W2973048552 title "Thermodynamic modeling and artificial neural network of air counterflow vortex tubes" @default.
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• W2973048552 doi "https://doi.org/10.1016/j.ijthermalsci.2019.106097" @default.
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